


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							A_      (       unittest.TestCase ):


  @property
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     torch.manual_seed(0    )
     _lowerCamelCase   :				List[Any]							=     UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=("DownBlock2D", "AttnDownBlock2D")							,up_block_types=("AttnUpBlock2D", "UpBlock2D")							,)
     return model






  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     self.dummy_uncond_unet
     _lowerCamelCase   :				Optional[Any]							=     ScoreSdeVeScheduler()

     _lowerCamelCase   :				str							=     ScoreSdeVePipeline(unet=__lowerCAmelCase							,scheduler=__lowerCAmelCase    )
     sde_ve.to(__lowerCAmelCase    )
     sde_ve.set_progress_bar_config(disable=__lowerCAmelCase    )

     _lowerCamelCase   :				Dict							=     torch.manual_seed(0    )
     _lowerCamelCase   :				List[Any]							=     sde_ve(num_inference_steps=2							,output_type="numpy"							,generator=__lowerCAmelCase    ).images

     _lowerCamelCase   :				Any							=     torch.manual_seed(0    )
     _lowerCamelCase   :				int							=     sde_ve(num_inference_steps=2							,output_type="numpy"							,generator=__lowerCAmelCase							,return_dict=__lowerCAmelCase    )[
         0
     ]

     _lowerCamelCase   :				Tuple							=     image[0, -3:, -3:, -1]
     _lowerCamelCase   :				Any							=     image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _lowerCamelCase   :				List[str]							=     np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2




@slow
@require_torch
class 							A_      (       unittest.TestCase ):


  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[Any]							=     "google/ncsnpp-church-256"
     _lowerCamelCase   :				str							=     UNetaDModel.from_pretrained(__lowerCAmelCase    )

     _lowerCamelCase   :				Optional[Any]							=     ScoreSdeVeScheduler.from_pretrained(__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     ScoreSdeVePipeline(unet=__lowerCAmelCase							,scheduler=__lowerCAmelCase    )
     sde_ve.to(__lowerCAmelCase    )
     sde_ve.set_progress_bar_config(disable=__lowerCAmelCase    )

     _lowerCamelCase   :				str							=     torch.manual_seed(0    )
     _lowerCamelCase   :				Tuple							=     sde_ve(num_inference_steps=10							,output_type="numpy"							,generator=__lowerCAmelCase    ).images

     _lowerCamelCase   :				int							=     image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)

     _lowerCamelCase   :				Optional[int]							=     np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

     assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


